Development of behavioral rules for upstream orientation of fish in confined space

Improving the effectiveness of fishways requires a better understanding of fish behavior near hydraulic structures, especially of upstream orientation. One of the most promising approaches to this problem is the use of model behavioral rules. We developed a three-dimensional individual-based model based on observed brown trout (Salmo trutta fario) movement in a laboratory flume and tested it against two hydraulically different flume setups. We used the model to examine which of five behavioral rule versions would best explain upstream trout orientation. The versions differed in the stimulus for swim angle selection. The baseline stimulus was positive rheotaxis with a random component. It was supplemented by attraction towards either lower velocity magnitude, constant turbulence kinetic energy, increased flow acceleration, or shorter wall distance. We found that the baseline stimulus version already explained large parts of the observed behavior. Mixed results for velocity magnitude, turbulence kinetic energy, and flow acceleration indicated that the brown trout did not orient primarily by means of these flow features. The wall distance version produced significantly improved results, suggesting that wall distance was the dominant orientation stimulus for brown trout in our hydraulic conditions. The absolute root mean square error (RMSE) was small for the best parameter set (RMSE = 9 for setup 1, RMSE = 6 for setup 2). Our best explanation for these results is dominance of the visual sense favored by absence of challenging hydraulic stimuli. We conclude that under similar conditions (moderate flow and visible walls), wall distance could be a relevant stimulus in confined space, particularly for fishway studies and design in IBMs, laboratory, and the field.


Abstract:
Improving the effectiveness of fishways requires a better understanding of fish behavior near hydraulic structures, especially of orientation. One of the most promising approaches to this problem is the use of model behavioral rules.
We developed a three-dimensional individual-based model based on observed brown trout ( Salmo trutta fario ) movement in a laboratory flume. We tested it against two flume setups with different discharges. We used the model to examine which of five behavioral rule versions would best explain upstream trout orientation. Versions differed in the stimulus-response mechanism for swim angle selection. The baseline stimulus was positive rheotaxis. It was supplemented by attraction towards either lower velocity magnitude, constant turbulence kinetic energy, increased flow acceleration, or smaller wall distance. As expected, modeling rheotaxis was central to matching the laboratory observations. In combination with a random component for swim angle selection, the baseline model version was already able to explain large parts of the observed behavior. Mixed results for velocity magnitude, turbulence kinetic energy, and flow acceleration supported the hypothesis that live trout did not orient primarily by means of flow features characterized by these variables, probably because the flow field did not challenge them physically.
Significantly improved results suggested that wall distance was the dominant orientation stimulus in our conditions. The absolute root mean square error (RMSE) was small for the best parameter set (RMSE = 9 for setup 1, RMSE = 6 for setup 2). We show that wall distance was most likely perceived visually. For dark or turbid water, also acoustic or tactile reception of walls would be conceivable. We conclude that wall distance estimation should be considered more frequently as an orientation stimulus in confined space. This is true wherever conditions enable wall distance perception, either in individual-based models, laboratory flumes, or in the field.  For model development, we used trout observation data obtained by, but not used within, a study currently under review (Schütz et al. 2021).
In this study, all care and procedures involving handling and holding fish were conducted as stated and permitted by the district government Karlsruhe (license AZ 35-9185.82/A-6/16). Improving the effectiveness of fishways requires a better understanding of fish behavior near 10 hydraulic structures, especially of orientation. One of the most promising approaches to this problem is 11 the use of model behavioral rules. 12 We developed a three-dimensional individual-based model based on observed brown trout (Salmo 13 trutta fario) movement in a laboratory flume. We tested it against two flume setups with different 14 discharges. We used the model to examine which of five behavioral rule versions would best explain 15 upstream trout orientation. Versions differed in the stimulus-response mechanism for swim angle 16 selection. The baseline stimulus was positive rheotaxis. It was supplemented by attraction towards either 17 lower velocity magnitude, constant turbulence kinetic energy, increased flow acceleration, or smaller wall 18 distance. 19 As expected, modeling rheotaxis was central to matching the laboratory observations. In combination 20 with a random component for swim angle selection, the baseline model version was already able to 21 explain large parts of the observed behavior. Mixed results for velocity magnitude, turbulence kinetic 22 energy, and flow acceleration supported the hypothesis that live trout did not orient primarily by means 23 of flow features characterized by these variables, probably because the flow field did not challenge them 24 physically. 25 Significantly improved results suggested that wall distance was the dominant orientation stimulus in 26 our conditions. The absolute root mean square error (RMSE) was small for the best parameter set 27 (RMSE = 9 for setup 1, RMSE = 6 for setup 2). We show that wall distance was most likely perceived 28 visually. For dark or turbid water, also acoustic or tactile reception of walls would be conceivable. We 29 conclude that wall distance estimation should be considered more frequently as an orientation stimulus in 30 confined space. This is true wherever conditions enable wall distance perception, either in individual-31 based models, laboratory flumes, or in the field.  Velocity is defined by direction and magnitude. Velocity direction is a fundamental stimulus in most 52 studies, as rheotaxis is crucial for guiding both upstream and downstream navigation (Arnold 1974;Elder 53 and Coombs 2015). Velocity magnitude is also commonly considered, most prominently for ascent (e.g., 54 Padgett et al. 2020). Since ascent requires energy, fish have developed strategies to reduce their effort 55 (Kerr et al. 2016). One strategy is to avoid zones of higher velocity for moving and holding (low-velocity 56 seeking), as energy demand is proportional to the cube of relative fish velocity (Wang and Chanson 2018).  (Goettel et al. 2015). Brown trout preferred areas of low drag, which was a better 66 explanation than TKE, turbulence intensity, and τxy in one study (Kerr et al. 2016). In an IBM, two trout 67 paths were best reproduced by avoiding areas of low and high TKE (Gao et al. 2016). In a follow-up model, 68 TKE, velocity magnitude, and strain rate were combined, with the former two having larger influence than 69 the latter on the tracks of three silver carp (Tan et al. 2018). 70 Advective acceleration was applied several times as an IBM stimulus for guiding juvenile salmon   120 We processed the trout track data using patterns in the sense of pattern-oriented modeling (Grimm 121 and Railsback 2012) using MATLAB R2018b. We defined five patterns, P1-P5, which capture the most 122 striking spatial behaviors in the whole domain: where a was a pattern weight factor chosen per pattern importance (a1 = 0.3, a2 = 0.1, a3-5 = 0.2), In total, the model had 7 fixed and D = 20 variable parameters ( Table 2 in results and Table 3   influence measure, μ*, and an interaction measure, σM, for each parameter. We used μ* for ranking 303 parameters by influence. For further interpretation, the values of the measures needed to be classified as 304 "high" or "low". This is usually achieved graphically (e.g., Campolongo et al. 2007 310 We focused on testing external stimuli, as they can be readily predicted. This is a necessary 311 requirement for our long-term goal of improving fishway performance. To find the stimulus which best 312 explained upstream orientation, we tested all five versions of the migrating behavioral rule in both setups.     400 We contrasted five stimulus versions for upstream swim angle selection in the model and ranked   Wall distance was the best stimulus version in both setups. Acceleration and velocity differed widely 415 in their ranks between setups, while baseline and TKE did not. In setup 1, the order of acceleration and 416 baseline was not distinct, as well as the order of TKE and velocity ( Figure 5). This is reflected in our 417 qualitative "rating"/classification used for discussion (Table 3). The estimated distributions of the RMSE 418 results are mainly multi-modal, which was the reason to rank them by their 10 th percentile. The overall 419 better results in setup 2 indicate that it was less demanding on the model than setup 1. 420 The wall distance version with its best parameter set reproduced all five patterns with high accuracy 421 (RMSE = 9 for setup 1, RMSE = 6 for setup 2) ( Figure 5, Figure 6). For comparison, e.g. Goodwin et al. See appendices S1 and S2 for observed trout movement data.  still not be as good as the first-ranked wall distance version. In addition, both stimuli were likely not real 532 stimuli (as discussed above) and hence such a model version would reduce robustness and explanatory 533 power. We conclude that our current model is not likely improved through stimuli combinations, but they 534 could be important for transfer to more complex environments such as in-situ fishways. For patterns P3 and P4, we did not find matching descriptions in the literature, because they 549 represent a new way of systematically dealing with the striking feature "turns". P5 was also too specific to 550 our setup for a meaningful comparison. 551 From the literature comparisons, it seems plausible that patterns P1 and P2 would be universal 552 enough to support model transfer to similar flows, e.g. to an altered flume geometry. Transfer to the real 553 world could be challenging, e.g. due to limited wall distance estimation and/or larger flow velocities.

Column name Definition Units
fish_id "1e" is setup 1; "2c" is setup 2. Next part of the ID is trial number, and last is fish number (3 per trial).